Method and apparatus for detection of cancerous and precancerous conditions in a breast

ABSTRACT

An apparatus for detection of precancerous and cancerous conditions in a breast is provided and includes a computer for digitizing at least one of a mammogram image data and a (scinti)mammogram image data of a breast, computer-based software operably associated with the computer based means for manipulating at least one of said digitized mammogram image data and said digitized (scinti)mammogram image data and generating at least one of a mammogram ROI image and a (scinti)mammogram ROI image data indicative of precancerous and cancerous condition, and monitor operably associated with the computer based means for concurrently visually displaying at least one of said digitized mammogram image data and said digitized (scinti)mammogram image data and said mammogram ROI image and said (scinti)mammogram ROI image data in a superimposed manner.

This Application is a Continuation-In-Part of U.S. Ser. No. 08/681,027,filed Jul. 22, 1996, now abandoned; which is a Continuation of U.S. Ser.No. 08/168,524, filed Dec. 15, 1993, now abandoned and a Continuation ofU.S. Ser. No. 08/168,081, filed Dec. 15, 1993, now abandoned; which areDivisionals of U.S. Ser. No. 07/768,373, filed Sep. 27, 1991, now U.S.Pat. No. 5,301,681.

BACKGROUND OF THE INVENTION

This invention is directed to a method and apparatus for detection of anabnormal condition in breasts, and more particularly to, but not by wayof limitation, a method and apparatus for detection of cancerous andprecancerous conditions in breasts, as well as established normal breastconditions.

Presently, there exist several techniques for detecting breast cancer.These techniques attempt to provide a physician with information basedon either anatomical or physiological anomalies to enable the physicianto make a determination as to the condition of the breast and both ofthese are limited.

Radiologists fail to detect cancer in up to thirty percent of patientswith breast cancer. Also, the malignancies missed by the radiologistsare evident in two thirds of the mammograms. There is a need to furtherassist radiologists, surgeons and other physicians in detecting,diagnosing, successfully biopsing and operating on precancerous andcancerous conditions.

It is known that areas of mammalian tissue adjacent to carcinomasexhibit increased temperature from that exhibited contemporaneously bynon-adjacent, non-cancerous areas. The temperature of thecancer-affected areas can fluctuate several degrees Centigrade fromnormal tissue; these differences having been demonstrated whilemonitoring such areas for a 24-hour period (one circadian cycle).

It had been thought that an abnormal temperature pattern associated witha tumor is a product of accelerated metabolism and numerous otherfactors, such as vaso-active substances and hormonal changes. Evidencenow suggests that local metabolic heat generation may be a second ordereffect since the majority of thermal signals are related to the functionof increased regional blood flow caused by local angiogenesis. A slightoverall increase in the temperature of the surrounding tissue, forinstance in localized areas of a woman's breast, can occur and isusually related to the vascular convection of heat that occurs as aresult of capillary dilatation and the secondary increase in blood flowcoupled with the higher temperature of the blood derived from thevascular bed and the possible vasodilator effect of catabolic productsof a tumor metabolism. These vascular manifestations of heat productionor cooling are of prime importance in the detection of subclinical orminimal cancers.

In addition, it is also known that tissue surrounding malignant lesionsof the breast often contain groups of microcalcifications withdiscernible morphology and structure which can be detected and utilizedin detecting breast cancers.

One prior device used for detecting cancer is a brassiere which includesa plurality of temperature sensors, an analog multiplexer circuit, acontrol circuit, a sample and hold circuit, an analog/digital converter,a buffer register, a storage register, a clock and a data logger. Thedevice allows for the storage of temperature readings in a digital form.This digital data may be uploaded to the data logger which converts thedigital signals to decimal form so that the temperature differences maybe read and analyzed by a supervising physician.

Several problems exist with the brassiere device. The brassieres must becapable of fitting a full range of breast sizes since tissue contact isessential to provide acceptable device performance. Also, this systemwould be expensive requiring individual brassieres to be prepared foreach user since it is unlikely that an individual would wear a brassierewhich was previously worn by another person for extended periods of timedue to the nature of the device. Thus, a disposable brassiere would bedesired.

Furthermore, the temperature sensors of the brassiere device are affixedon its inner surface. Ideally, all sensors are in contact with the skinwhen the brassiere is positioned about the breast. Realistically,however, in the normal course of wear, the sensors will frequently notbe in contact with the skin. Lack of contact causes the sensors toproduce false skin temperature readings. It is also noted that suchdevice does not disclose a need or means for calibrating the sensors.Any diagnosis based on uncalibrated sensor readings could be faulty.

Devices which use a passive thermographic analytical apparatus provide adirect readout of the results through analysis of a thermographicradiation pattern of the human body. Such devices include a matrix ofinfrared energy sensors and reflectors which are mounted in a closed,spaced array to produce a pattern of temperature measurements of thealigned areas of the body. The sensors simultaneously or sequentiallyread a thermographic pattern and develop analog signals which areconverted into the appropriate digital form and are stored in a memory.The digital signals are then analyzed by a central processing unit (CPU)in accordance with a particular spatial pattern recognition softwareprogram. The program includes an algorithm having a number of parametersused in comparing differences in temperatures throughout the breasts togive a probability of breast normality or abnormality.

Unfortunately, such devices are unable to detect small tumors on theorder of less than 0.5 cm and possibly other larger tumors as well,especially certain types of cancers. This seems to be due to theresolution and sensitivity capabilities of the thermographic sensors.Another problem with such devices is that the CPU will give inaccurateresults if internal failure occurs at any point in the computer'sprobability program. Faulty readings from the thermographic patterncause the software program to generate inaccurate results.

Of even greater concern, such thermographic devices do not take intoaccount the chaotic fluctuation of normal body temperatures over timeand between locations on the body. The temperatures between the left andright breasts may vary as much as 4 degrees Centigrade during anyonecircadian cycle, as well as constant fluctuation throughout any 24-hourperiod. Since the patient is required to remain in front of the scanningapparatus of the thermographic device for only a short period of time inorder to take a thermographic picture, which picture only represents onemoment in time and is not representative of the actual condition of thebreast over a long period of time. An analysis based on suchthermographic results could be totally inaccurate.

As previously mentioned, one common and widely used technique fordetermining existence of breast cancer is mammography. This radiologicaltechnique passes ionizing radiation through the breast, which is per seinvasive, to produce a radiograph which should report tumors as darkenedareas. This method of detecting breast cancer is limited by the age ofthe patient and condition of the tissue examined. In addition, aboutsixty-six percent of cancer is missed by the radiologists in retrospect.Most cancer is diagnosed too late and successful diagnosis and treatmentare more attainable if the cancer is found at early stages. If thetissue is dense, as is characteristic of breast tissue in younger women,the image produced is more uniform in gray scale causing detection oftumors to be more difficult.

In cases of breast implants, thirty percent of the breast may beundiagnosable. This is due to the visual and mammographic impairment inmany cases.

Even though such prior devices have been somewhat effective, thereremains a need to improve the method and device for detection ofpotentially cancerous conditions in breasts.

SUMMARY OF THE INVENTION

It is an object of the present invention to provide an improved methodand device for detecting cancer.

It is yet another object of the present invention to provide a methodand a device for an identification of regions of interest (ROI) inmammograms and (scinti) mammograms for detecting cancerous andprecancerous conditions.

Another object is to assist physicians in doing surgery and biopsies onthe breast.

The present invention is directed to a method and apparatus fordetection of cancerous and precancerous conditions in breasts. Theapparatus comprises computer-based means for digitizing a mammogram or(scinti)mammogram of a breast, first means operably associated with thecomputer-based means for manipulating the digitized mammogram image dataand/or digitized (scinti)mammogram image data to generate mammographicROI image data and/or (scinti)mammographic image ROI data, and meansoperably associated with the computer-based means for concurrentlydisplaying the digitized mammogram image data and/or digitized(scinti)mammogram image data and the mammographic ROI image data and/or(scinti)mammographic ROI data.

The digitizing means includes a scanner operably connected to a CPU. Thefirst manipulating means includes means for filtering, segmenting, andregionalizing the digitized (scinti)mammogram image to produce the ROIdata. The first manipulating means further includes a neural network.

The invention further includes means for sensing at least one of breasttemperatures and electro-physiological data and generating signals inresponse thereto, second means operatively associated with the sensingmeans for receiving and manipulating the signals for generating at leastone of therm-physiological data and electro-physiological dataindicative of precancerous and cancerous conditions in the breast andwherein the display means concurrently displays the digitized mammogramimage data and/or digitized (scinti)mammogram image data, themammographic ROI image data and/or (scinti)mammographic ROI image dataand at least one of the thermo-physiological data andelectro-physiological data in a superimposed manner. The displayingmeans further includes means for associating a unique visual attributefor at least one of the digitized mammogram image data, digitized(scinti)mammogram image data, the mammographic ROI image data, the(scinti)mammographic ROI image data, the thermo-physiological ROI dataand the electro-physiological ROI data.

The receiving and manipulating means includes processing means havingnon-algorithmic logic which utilizes prior pathological data incorrespondence with the sensed signals to manipulate the signals andproduce the thermo-physiological data and the electro-physiologicaldata.

The manipulating means may further include a neural network havingpredetermined solution space memory, the solution space memory includingregions indicative of cancerous and non-cancerous conditions, whereinthe thermo-physiological data and the electro-physiological dataproduces the signals being projected into said regions.

The device further includes means operatively connecting the sensingmeans and the receiving and manipulating means, the connecting meansincludes means for receiving and storing the signals from the sensingmeans, means for controlling transmission of the signals from thesensing means to the storing means, and means for calibrating thesensing means for use in generating the thermo-physiological data.

Also, provided is a method for determining cancerous conditions in abreast. The method comprises the steps of (a) digitizing a mammogram or(scinti)mammogram, (b) manipulating the digitized data to producemammographic ROI image data and/or (scinti)mammographic ROI image dataindicative of precancerous and cancerous conditions in the breast, (c)digitizing at least one of thermo-physiological data signals andelectro-physiological data signals taken from the breast into at leastone of the thermo-physiological digitized data and theelectro-physiological digitized data (d) manipulating the at least oneof the thermo-physiological digitized data and the electro-physiologicaldigitized data for generating at least one of a thermo-physiological ROIdata and an electro-phisiological ROI data about the signals indicativeof precancerous and cancerous conditions in the breast and (e)concurrently displaying the mammographic ROI image data and/or(scinti)mammographic ROI image data and at least one of thethermo-physiological ROI data and the electro-physiological ROI data ona suitable display monitor such as a high resolution CRT.

The present invention is more particularly described in the drawings andspecification which follow. Other objects and advantages will be morereadily apparent upon reading the following.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows the template of the present invention.

FIG. 2 shows the sensors and harness block of the present invention.

FIG. 3 is a schematic drawing of a part of the present invention.

FIG. 4 is a general diagram of the neural network of the presentinvention.

FIG. 5 represents a neuron-like unit of the present invention.

FIG. 6 discloses a schematic of the device of the present invention.

FIG. 7 shows a block diagram of the steps performed by the method of thepresent invention.

FIG. 8 shows a display depicting mammographic image data and/or(scinti)mammographic image data and one of the mammographic ROI imagedata, the (scinti)mammographic ROI image data, the thermo-physiologicalROI data and electro-physiological ROI data in a superimposed manner.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

FIG. 6 generally depicts the apparatus of the present invention and isgenerally represented by the numeral 100. The apparatus 100 generallyincludes a CPU 44, a scanning device 102 operably connected to the CPU44, a display 48 operably connected to the CPU 44 and harness 30 withits electro physiological/thermo-physiological sensors 21-28 as morefully set forth herein below.

The scanning device 102 should be of a type which scans at a rate fordigitization, preferably of about 0.1 mm per pixel. An Imagitex model1085 (Nashua, N.H.) has been found suitable which is capable of1024×1024 pixel optimal resolution. Each pixel can be assigned one of255 intensity levels (8 bits). Higher bit resolution, such as 10 or 12,may be desirable. With the stated scan rate, resolution ofcalcifications as small as 0.1×0.1 mm is readily attainable.

The CPU 44 has a memory 104 for storing a digitized mammogram image dataor digitized (scinti)mammogram image data produced via scanning amammogram image data or a (scinti)mammogram image data using thescanning device 102. The memory 104 also includes a filtering device106, preferably resident on the memory 104 in the form of software. Thefiltering device 106 includes non-linear filtering software, such asconventional median filtering, to reduce noise in the digitization ofmammogram image data or (scinti)mammogram image data. Median filtering,which utilizes the median value of a neighborhood of pixels, is employedas an area process utilizing either a box or cross kernel ascertainableto a person skilled in the art, and is useful in minimizing linedisplacement and preserving sharpness of edges, lines and corners. Thesesurface structures are required for an effective analysis ofmicrocalcification morphology. Pixel intensity changes are efficientlydetected using a two dimensional difference of Gaussian or Laplacian ofGaussian filter kernel, based on the anticipated detection size of thetarget microcalcification. Here, the Gaussian techniques destroy anyunwanted spatial resolution that fall outside the parameters of theGaussian filter. The Difference or Laplacian operator can then detectintensity changes more readily from the remaining digitized mammogramimage data and/or the digitized (scinti)mammogram image data.

The filtering device 106 also includes a segmenting software, utilizingadaptive thresholding, wherein each scanned pixel of the digitizationmammogram image data and/or (scinti)mammogram image data is evaluated tosee whether it meets a predetermined intensity threshold. Each pixel isfurther evaluated to determine whether the pixel is within an acceptableintensity offset to the surrounding tissue. The "offset" is the averageintensity of pixels surrounding, but excluding the identified pixel in apredetermined pixel neighborhood. The average intensity is subtractedfrom the individual pixel intensity and the difference compared to apredetermined threshold range. A ratio test is applied to determine ifthe intensity ratio of the pixel to the average value in the immediatesurrounding area meets a minimum threshold. If each of the criteria ismet, the pixel is associated as being part of a calcification.

Each pixel associated as being part of a calcification is identified andgrouped (in clusters with adjacent pixels) as microcalcifications. Amaximum dimension for each microcalcification, the surrounding tissueand other suspicious areas and lesions are determined and compared to anallowable microcalcification size defined by a predetermined maximum andminimum range.

Intensity gradients are also computed for each pixel and its surroundingpixels in the cluster and outside the cluster. The gradient is computedand compared with predetermined mean and variance criteria. The minimaldistance center of the calcification pixel cluster is then identified aswell as measurements of tissue around microcalcifcations and itsmorphology.

Regions of clusters can be defined by sets of pixels in separatematrices, wherein each matrix may include one or more pixel. If thenumber of detected microcalcifications exceeds a predetermined thresholdper unit volume (e.g., 3 microcalcifications in a 1 cm cubic volume), acluster of microcalcifications is identified. A pixel-inclusionoperation is implemented which is governed by a dynamic test forinclusion based on the average intensity value and variance of pixelscontained in the connected cluster, wherein each pixel can only be amember of one matrix.

Clusters are then analyzed using certain rules (i.e., neural networklogic as described herein below). Clusters which are found to meetcertain predetermined criteria are marked as "clinically significant"and noted by the degree of suspicion by color or graphic coding todenote the degree or amount of likelihood of probability of breastdisease.

Clinically significant microcalcifications are then determined to beeither benign or malignant. This determination is made with the aid of afurther software filter, such as fractal-based algorithm or a neuralnetwork of a similar type described below, which looks at themicrocalcification morphology. In addition to morphology, the averagepixel intensity, region ratio averages, offset region averages andmicrocalcification sizes and dimension ratios can all be utilized ingenerating mammogram ROI image data and/or (scinti)mammographic ROIimage data.

Fractal analysis as well as wavelet analysis of the above factorsprovides a window data of the image immediately surrounding themicrocalcification clusters and helps the radiologist in evaluating themammographic ROI image data and/or (scinti)mammographic ROI image data.The display monitor 48 is utilized here to display the mammographicimage data and/or (scinti)mammographic image data with a superimposedROI image data as seen in FIG. 8. The CPU 44 associates a unique visualattribute for display by the display monitor 48 with each of themammographic image data and/or (scinti)mammogram image data and themammographic ROI image data and/or (scinti)mammographic ROI image datato aid in distinguishing the same. Particularly, a window is associatedwith mammographic ROI image data and/or (scinti)mammographic ROI imagedata signal and displayed as pseudo-colors superimposed over themammographic image data and/or (scinti)mammographic image (e.g., in agray scale, for example) which indicate probability of malignancy.

FIG. 1 illustrates template 10. The template 10 is made of a materialcapable of conforming to a mammalian breast and surrounding breasttissue on the chest wall area. When the template is properly oriented,the template 10 has a plurality of openings 12 and 13 which arespatially positioned in accordance with areas of the breastpathologically determined to be highly susceptible to cancerdevelopment. As shown in FIG. 1, the template 10 has a center portion 14and four arms 16a-d extending radially outward from central portion 14.The template 10 is of a sufficient size to accommodate numerous breastsizes. Central portion 14 has the opening 12 centrally defined thereinwhich is placed over the breast nipple to position the template. Eacharm 16a-d has a sufficient quantity of the openings 13 to accommodatediffering breast sizes such that at least one opening per arm can beplaced over a breast area desired to be sensed.

Areas of the breast to be sensed, for example, are those where cancerdevelopment is most likely to occur, can be marked by orienting thetemplate 10 on a breast as shown in FIG. 1. Arms 16a and 16b extendalong the vertical midline 50 of the breast with arm 16a above and arm16b below the horizontal midline 51 of the breast. Arms 16c and 16dextend diagonally to bisect the upper outer quadrant and upper innerquadrant of the breast, respectively. On the patient's other breast, theposition of arms 16c and 16d are reversed which allows for symmetricalmarkings of the breasts, important in obtaining an accurate diagnosis.

Once location of desired sensor placement has been determined, thesensors 21-28 (in the preferred embodiment the sensors are thermistors)are affixed to the breast tissue. FIG. 2 illustrateselectro-physiological/thermo-physiological sensors (thermistors) 21L-28Land 21R-28R positioned on the breasts and attached to harness block 30according to the invention. Thermistors 21L-28L and 21R-28R arepositioned over the marked areas of the right and left breasts whichhave been determined by use of template 10. Specifically, thermistors21L-28L and 21R-28R are placed on the breasts as follows: 21L and 21Rbelow the nipple; 22L and 22R in the upper outer quadrant; 23L and 23Rin the upper outer quadrant toward the axilla; 24L and 24 R on the upperareola; 25L and 25R on vertical midline 50' above horizontal midline51'; 26L and 26R in the upper inner quadrant; 27L in an ambienttemperature zone; 27R on the sternum; 28L and 28R on other areas ofconcern and at contralateral positions. Each pair of the thermistors(e.g., 21L/21R, 22L/22R, etc.) is preferably marked to allow for easyidentification of each thermistor pair as well as each thermistor. Forexample, each thermistor pair is color coded and tabbed with a numberand letter.

Each thermistor and its signals are consequently identified with aspecific position on the breast. This consistency simplifies subsequentprocessing and improves accuracy of the signals in terms of individualsignal correlation with calibration data and selection of specificsignal sources for manipulation in developing the generalization ofphysiological condition. This also simplifies correlation of resultswith specific sensor positions on the breast to arrive at a morespecific determination of the location of abnormal physiologicalcondition. While the number of thermistors and positioning arespecifically set forth, it is conceived that accuracy increases as thenumber of thermistors increases.

Each of the thermistors 21L-28L and 21R-28R is made of an electricallyconductive material (e.g. nickel-manganese oxide). Each thermistorproduces a resistance which varies with the temperature measured. Withrespect to obtaining thermo-physiological data, an important featureresides in the calibration system of the thermistors 21-28. In thepresent invention, the Steinhart and Hart equation, an empiricalexpression, has been determined to be a suitably desirable signaltransform algorithm for the resistance--temperature relationship. It isusually found explicit in T, with:

    1/T=a+b(LnR)+C(LnR)3

and in which T is the Kelvin temperature, Ln is the Logarithm of R tothe base e and a, b and c are coefficients derived from measurement.

Normalization coefficients a, b and c are found by making measurementsof R at three Temperatures (20 degrees, 30 degrees, and 40 degrees C.)and solving simultaneously:

    1/T=a+b(LnR1)+C(LnR1)3

    1/T2=a+b(LnR2)+C(LnR2)3

    1/T3=a+b(LnR3)+C(LnR3)3.

Over a temperature range of 20 degrees to 40 degrees Centigrade (C.),this algorithm produces an accurate fit. Because each thermistor hasslightly different physical and chemical properties, the resistance ofeach thermistor is measured at 20 degrees C., 30 degrees C., and 40degrees C. in a stirred nonconductive fluid bath, (e.g. BLANDOL™available from Sealand Chemical, Silicon oil or FLURINERT 40™ availablefrom Sealand Chemical), the fluid temperature measured by a NISTstandard thermometer, and the specific normalization coefficients foreach thermistor determined by inserting the resistance of eachthermistor at each temperature into and simultaneously solving theforegoing equations.

Referring to FIG. 3, these coefficients are referred to as calibrationdata which are stored in memory 31 (read only memory, for example) ofharness 30 and identified as relating to a specific thermistor. Animportant aspect of the present invention is that the calibration datais stored in harness block 30. In the field of use, many harness blockswill exist. Accessing each thermistor's calibration datacontemporaneously with its respective stored signals from the harnessblock will remove the potential for processing errors occurring frommismatching calibration data and thermistors.

Each of thermistors 21L-28L and 21R-28R is connected to an analogmultiplexer 32 which allows for simultaneous transmission of signalsfrom the thermistors 21L-28L and 21R-28R to analog/digital (A/D)converter 34. Each signal is then amplified by amplifier 35 and appliedto A/D converter 34 which converts each analog signal into a suitablemultiple bit binary number or digital word suitable for processing.

Within harness 30, an oscillator type system clock 40 supplies pulses toCPU 38. Upon receiving a predetermined number of pulses, CPU 38initiates multiplexer 32 to poll thermistors 21L-28L and 21R-28R bygating signals from each thermistor 21L-28L, 21R-28R. CPU 38 controlsthe transmission and storing of the signals in random access memory 36.Multiplexer 32, A/D converter 34, memory 36, CPU 38, clock 40, andamplifier 35 are of any suitable construction as is known in the art.

FIG. 3 shows the CPU 44 operatively associated with neural network 46and CPU 38. CPU 44 is capable of uploading data from memory 31 and 36and channeling the data through neural network 46. The CPU 44 can be acommercially available microprocessor which uses the software systemdescribed herein below. Alternatively, a commercially availablemicroprocessor can be integrated with a commercially availableneurocomputer accelerator board, such as the one available from ScienceApplications International Corp. (SAIC).

Neural network 46 utilizes parallel processing which allows quantitiesof information, or data, to be analyzed more quickly and in a differentfashion than is permitted in serial processing. Generally speaking,neural network paradigms make no assumptions about the data and featureextraction is automatically performed. The network itself selects,through a non-algorithmic process, features of the input data on whichit focuses at any point during data processing and manipulation. Wherethe desire is to receive output results related to normal or abnormalphysiological condition, the features are weighted based on pathologicalevidence and other empirical data introduced into the neural networkthrough what is referred to as a training process.

The neural network training process entails the creation of a solutionspace memory. Solution space memory as used herein refers tomultidimensional space created internal to the neural network containingregions associated with pathological determinations of non-cancerousnormal physiological condition and other regions associated withcancerous abnormal physiological conditions. The training processfurther entails iteratively entering empirical data, risk factors,imaging data and pathological evidence and modulating the neural networkbased upon its output. This iterative training accomplishes severalpurposes, first refining the definition of the regions associated witheach condition in the solution space memory, and second causing theneural network to determine the non-algorithmic process by which itprojects new data signals into the solution space. Following training,the neural network, will manipulate and project input signals into thesolution space memory and the resulting intersection of those signalswith one or more solution space regions produces a generalization aboutthe signal indicative of cancerous abnormal or non-cancerous normalphysiological condition. Provided that sufficient pathological and otherempirical data is available to train the neural network, itsmanipulation methodology, based on parallel processing, produces resultsconsistent with non-invasive and invasive pathological determinationsmade through mammography, biopsy techniques and surgical techniques. Thedisplay 48 is connected to the CPU 44 such that the display 48 providesvisual display of the results generated by the neural network 46 and CPU44 based upon the input data received.

As shown in FIG. 4, the neural network 46 includes at least one layer oftrained neuron-like units, and preferably at least three layers. Theneural network 46 includes input layer 55, hidden layer 52, and outputlayer 54. Each of the input, hidden, and output layers include aplurality of trained neuron-like units.

The neuron-like units of the input layer include a receiving channel forreceiving a sensed signal, wherein the receiving channel includes apredetermined modulator for modulating the signal. The neuron-like unitsof the hidden layer are individually receptively connected to each ofthe units of the input layer. Each connection 67 includes apredetermined modulator for modulating each connection between the inputlayer and the hidden layer.

The neuron-like units of the output layer are individually receptivelyconnected to each of the units of the hidden layer. Each connection 67includes a predetermined modulator for modulating each connectionbetween the hidden layer and the output layer. Each unit of said outputlayer includes an outgoing channel for transmitting the modulatedsignal.

Referring to FIG. 5, each trained neuron-like unit 56 includes adendrite-like unit 58, and preferably several, for receiving digitizedincoming signals. Each dendrite-like unit 58 includes a particularmodulator 60 which modulates the amount of weight which is to be givento the characteristic sensed by modulating the incoming signal andsubsequently transmitting a modified signal. For software, thedendrite-like unit 58 includes an input variable X_(a) and a weightvalue W_(a) wherein the connection strength is modified by multiplyingthe variables together. For hardware, the dendrite-like unit 58 can be awire, optical or electrical transducer having a chemically, optically orelectrically modified resistor therein.

Each neuron-like unit 56 includes soma-like unit 62 which has athreshold barrier defined therein for the particular characteristicsensed. When the soma-like unit 62 receives the modified signal, thissignal must overcome the threshold barrier whereupon a resulting signalis formed. For software, the soma-like unit 62 is represented by the sumS=(Σ_(a) X_(a) ^(*) W_(a))-β, where β is the threshold barrier. This sumis employed in a Nonlinear Transfer Function (NTF) as defined below. Forhardware, the soma-like unit 62 includes a wire having a resistor; thewires terminating in a common point which feeds into an operationalamplifier having a nonlinear part which can be a semiconductor, diode,or transistor.

The neuron-like unit 56 includes an axon-like unit 64 through which theoutput signal travels, and also includes at least one bouton-like unit66, and preferably several, which receive the output signal fromaxon-like unit 64. Bouton/dendrite linkages form the connection 67 fromthe input layer to the hidden layer and from the hidden layer to theoutput layer. For software, the axon-like unit 64 is a variable which isset equal to the value obtained through the NTF and the bouton-like unit66 is a function which assigns such value to a dendrite-like unit of theadjacent layer. For hardware, the axon-like unit 64 and bouton-like unit66 can be a wire, an optical or electrical transmitter.

The modulators of the input layer modulate the amount of weight to begiven various physiological characteristics such as, but not necessarilylimited to, temperature, temperature fluctuation, area of the bodysensed, physiological period of month (menstrual), and pre orperimenopausal status, morphology, size, intensity and number of thetissue calcifications.

For example, if a patient's tissue temperature is higher than, lowerthan, or in accordance with what has been predetermined as normal, thesoma-like unit would account for this in its output signal and thisbears directly on the neural network's decision to indicate whether anormal non-cancerous or an abnormal precancerous or cancerous conditionexists.

The modulators of the output layer modulate the amount of weight to begiven for indicating normal non-cancerous physiological conditions orabnormal precancerous or cancerous physiological conditions. It is alsopossible, however, to assign weighting unit values to output neuron-likeunits which represent a probability of normal or abnormal conditions,e.g. 90 percent likely to be abnormal cancerous conditions. It is notexactly understood what weight is to be given to characteristics whichare modified by the modulators of the hidden layer, as these modulatorsare derived through a training process defined below.

The training process is the initial process which the neural networkmust undergo in order to obtain and assign appropriate weight values foreach modulator. Initially, the modulators and the threshold barrier areassigned small random non-zero values. The modulators can be assignedthe same value but the neural network's learning rate is best maximizedif random values are chosen. Pathological and other empirical data takenfrom control group subjects over one period up to forty-eight hours atpredetermined intervals is input in parallel into the dendrite-likeunits of the input layer and the output observed.

The NTF employs S in the following equation to arrive at the output:##EQU1## For example, in order to determine the weight to be given toeach modulator for the particular physiological variable, the NTF isemployed as follows:

If the NTF approaches 1, the soma-like unit produces an output signalindicating an abnormal condition. If the NTF approaches 0, the soma-likeunit produces an output signal indicating a normal condition. If theoutput signal clearly conflicts with the known condition, pathologicallydetermined, an error occurs. The weight values of each modulator arethen adjusted using the following formulas so that the input dataproduces the desired empirical output signal.

For the output layer:

W^(*) _(kol) =W_(kol) +GE_(k) Z_(kos)

W^(*) _(kol) =new weight value for neuron-like unit k of the outputlayer.

W_(kol) =actual weight value obtained for neuron-like unit k of theoutput layer.

G=gain factor (usually less than 1.0)

Z_(kos) =current output signal of neuron-like unit k of output layer.

D_(kos) =desired output signal of neuron-like unit k of output layer.

E_(k) =Z_(kos) (1-Z_(kos))(D_(kos) -Z_(kos)), (this is an error termcorresponding to neuron-like unit k of output layer).

For the hidden layer:

W^(*) _(jhl) =W_(jhl) +GE_(j) Y_(jos)

W^(*) _(jhl) =new weight value for neuron-like unit j of the hiddenlayer.

W_(jhl) =current weight value obtained for neuron-like unit j of thehidden layer.

G=gain factor (generally less than 1)

Y_(jos) =actual output signal of neuron-like unit j of hidden layer.

E₁ =Y_(jos) (1-Y_(jos))Σ_(k) E_(k) W_(kol), (this is an error termcorresponding to neuron-like unit j of hidden layer over all k units).

For the input layer:

W^(*) _(iil) =W_(iil) +GE_(i) X_(ios)

W^(*) _(iil) =new weight value for neuron-like unit i of input layer.

W_(iil) =current weight value obtained for neuron-like unit i of inputlayer.

G=gain factor (generally less than 1)

X_(ios) =actual output signal of neuron-like unit i of input layer.

E_(i) =X_(ios) (1-X_(ios))Σ_(j) E_(t) W_(jhl), (this is an error termcorresponding to neuron-like unit i of input layer over all j units).

The process is iteratively repeated by entering further empirical datainto the neural network and observing the output signal. If the outputis in error with what the known output should be, the weights areadjusted in the manner described above. Utilizing input data known tocorrespond to actual physiological conditions pathologically determined,this process continues until the output is substantially consistent withsuch pathologically determined physiological conditions. The weights arethen fixed.

Upon fixing the weights of the modulators, a solution space memory withregions indicative of normal and abnormal physiological conditions isestablished in the neural network. The neural network is at this stageconsidered trained and can make generalizations about input data byprojecting input data into the solution space memory and determiningwhich regions the input data intersects. The generalization is improvedby comparing input data taken repetitively at intervals over a period oftime, in the present invention taking temperature readings every fiveminutes over one cycle, although it is recognized that a differentinterval may still provide reliable results particularly when coupledwith the other risk factors discussed herein as part of the presentinvention. The generalization relates not only to the breast as a wholebut also is capable of identifying abnormal physiological conditionswith a specific quadrant or other special region of the breast or breasttissue.

While the preferred embodiment has employed a multiple-layer backpropagation neural network to carry out the invention, it is conceivedthat other means, such as myriad alternative neural networkarchitectures or statistical programs, might be used instead of or inconjunction with the neural network. It is conceived that manyvariations, modifications and derivatives of the present invention arepossible and the preferred embodiment set forth above is not meant to belimiting of the full scope of the invention. It is also conceived thatthe present invention may likewise emlpoy other diagnostic techniqueswhich generate information which is likewise digitzable and used toproduce ROI image data for such information so that the same may bedisplayed in a superimposed manner to further aid the user.

The following example is presented for the purpose of illustrating thepresent invention, but is not intended to be limiting in the nature andscope of the present invention.

Method using Thermo-physiological Variations Aspect of the Invention

138 subjects were recruited from the population at a surgical oncologyclinics and hospitals who had been scheduled for open-breast biopsies asa result of physical exam and mammography. Each subject wore the harnessblock/sensors of the present invention for a period up forty-eight hourswherein data readings were taken every five minutes.

The data were analyzed using the neural network described above whichwas trained using fifty-seven exemplar cases. The following results wereobtained.

Total biopsies: 138

Positive for cancer: 23

Cancer found by mammogram: 19

Cancer found by present invention: 22

Palpable cancers: 17

Needle localization: 6

Three of the cancers detected by the present invention but not bymammography had sizes of 0.5, 0.7, and 2.0 cm in subjects aged 36, 38and 44 respectively. In addition, the present invention indicated anadditional 21 of the subjects as high risk to cancer development.

What is claimed is:
 1. A device for detecting breast cancer in amammalian breast, comprising:non-invasive means adherently connected tothe breast for sensing temperature over a circadian period in the breastcharacteristic of breast cancer formation and generating temperaturesignals in response thereto; and means for receiving and manipulatingsaid signals to produce an output signal about said signals indicativeof breast cancer formation in a region of said breast, wherein saidreceiving and manipulating means includes a neural network havingpredetermined solution space memory trained on prior breast temperaturesignals taken over a circadian period, wherein said temperature signalsare projected through said neural network to produce a generalizationabout said temperature signals, and wherein said generalization is usedto produce said output signal.
 2. The device of claim 1, wherein saidneural network includes:an input layer having a plurality of neuron-likeunits, wherein each neuron-like unit includes a receiving channel forreceiving said signals, wherein said receiving channel includespredetermined means for modulating said signal; a hidden layer having aplurality of neuron-like units individually receptively connected toeach of said units of said input layer, wherein each connection includespredetermined means for modulating each connection between said inputlayer and said hidden layer; and an output layer having a plurality ofneuron-like units individually receptively connected to each of saidneuron-like units of said hidden layer, wherein each connection includespredetermined means for modulating each connection between said hiddenlayer and said output layer, and wherein each unit of said output layerincludes an outgoing channel for projecting the modulated signal intosaid solution space memory.
 3. The device of claim 2, wherein saidneural network performs said generalization by manipulating andprojecting said signal into said solution space memory and identifyingwhether such projection intersects with one or more regions indicativeof abnormal physiological condition.
 4. The device of claim 1, whereinsaid regions of solution space memory of said neural network includeregions indicative of normal physiological conditions and other regionsindicative of abnormal physiological conditions.
 5. The device of claim4, wherein said neural network performs said generalization bymanipulating and projecting said signal into said solution space memoryand identifying which regions such projection intersects.
 6. The deviceof claim 1, which further includes means operatively connected to saidreceiving and manipulating means for displaying said generalization. 7.The device of claim 1, wherein said sensing means includes a pluralityof temperature sensors spatially positioned in accordance with areas ofthe breast which are highly susceptible to cancer development.
 8. Amethod for determining cancerous and precancerous conditions in amammalian breast comprising the steps of:(a) adhering means for sensingtemperature adjacent to a breast area which has been predeterminedhighly susceptible to cancer development and generating temperaturesignals over a circadian period; and (b) manipulating said temperaturesignals through a neural network trained on prior breast temperaturesignals taken over a circadian period to produce an output signalindicative of cancerous and precancerous conditions in said area of thebreast, wherein said neural network includes a predetermined solutionspace memory indicative of precancerous breast tissue and breast cancer.